The present invention finds particular application in conjunction with medical imaging, and especially in connection with gamma cameras and computed tomography equipment where it is necessary to account for negative data present in a positively constrained data set. It will be appreciated, however, that the invention will also have other applications.
Nuclear or gamma cameras are often used to measure gamma radiation emitted by a body under examination. One application of nuclear or gamma cameras is in medical imaging, where one or more radionuclides are introduced into a region of interest within a patient or other object being imaged. These radionuclides decay, thereby emitting gamma radiation characterized by photons having one or more characteristic energies.
By measuring the energy level and the location of the emitted photons, an image representative of the gamma radiation emitted from the body under examination can be created. Inasmuch as the image is based on a count of photons detected at each of a plurality of locations, each location is expected to have a non-negative value.
In practice, however, the radiation spectrum resulting from the decay of a radionuclide is spread over a range of energies. Compton interactions with electrons in the gamma camera scintillation crystal and the body being imaged contribute to this spread. Photons which experience Compton interactions are deflected in angle and experience an energy loss compared to primary (i.e. non-scattered) photons, but can be detected along with primary photons, thereby resulting in spurious scatter counts at energy levels below the primary photopeak.
Numerous techniques for correcting for Compton scatter have been implemented, for example, as described in Morgan, et al., U.S. application Ser. No. 08/561,936, Split Window Scatter Correction, filed Nov. 22, 1995, incorporated by reference herein. In general, these scatter correction techniques estimate the number of scatter counts at a given location and subtract the estimate from a total count value, thereby generating a corrected value. In some situations, a negative count value will be generated.
Various techniques for handling these negative counts have also been implemented. One technique is to truncate negative values to zero. Another technique is to add the largest negative magnitude to all of the elements in the data set, thus placing a zero at the location containing the most negative number. These techniques overestimate the number of counts that have been received, thereby biasing the resultant data set.
Yet another scheme is to apply a low pass filter to the data set. Because a low pass filter by definition decreases the rate of change of the data, edges and other features within the object being imaged are rendered less distinct. Thus, the application of a low pass filter also introduces unwanted errors.
Yet another technique for removing negativity artifacts in filtered back projection data sets was disclosed by O'Sullivan et at. in Reducing Negativity Artifacts in Emission Tomography: Post-Processing Filtered Backprojection Solutions, IEEE Transactions on Medical Imaging, Vol. 12, No. 4, December 1993. The disclosed technique involves scanning a post-processed image to locate a pixel having a negative value and locating the most positive pixel in the neighborhood of the pixel. The negative pixel is adjusted to the smaller of the sum of the pixels and zero. The positive pixel is adjusted to the larger of the sum of the pixels and zero. A new negative pixel is selected, and the process is repeated.
The disclosed thus distributes the negative artifact at a pixel in the surrounding neighborhood. By adjusting the value of the single most positive pixel, however, that pixel is likely to be over corrected, leading to inaccuracies in the corrected data set. The disclosed algorithm also fails to preserve the total number of counts where the most positive pixel in the neighborhood is not sufficiently positive to absorb the entire negative value of the located pixel.
In light of these shortcomings, a technique for handling negative data in a positively constrained data set which avoids the errors introduced by previous techniques is needed.